Bibliographic record
Abstract
This thesis examines key topics in macroeconomics, with an emphasis on firm dynamics, innovation, growth, and market power. In the first chapter, I study firm growth through geographic expansion and its implications for local product market competition. Using Canadian microdata, I document the rise in firms' geographic expansion and local competition from 2001-2018. To explain these trends, I build a dynamic general equilibrium model of firms' geographic expansion, incorporating multiple markets and local oligopolistic competition. Estimations reveal that higher innovation costs, a shift toward less productive entrants, and greater product differentiation are key drivers of the observed trends. I find that subsidizing the expansion of more productive and expansion-efficient firms can substantially increase efficiency and social welfare. Recent evidence shows a decline in local market concentration, despite rising national concentration in the United States. In the second chapter, I reconcile this divergence and explore its implications for consumer welfare using a model incorporating endogenous entry and markups. I show that lower market entry costs lead to increased entry into multiple markets, reducing local concentration, while also decreasing the total number of firms, raising national concentration. Calibration of the model shows that a 10% reduction in market entry costs increases the number of firms in a market by 4.38%, reduces the number of firms in the economy by 0.01%, and increases aggregate consumption and real wages by 2.36%. Cash utilization in U.S. merger and acquisition (M&A) transactions has increased over 50% since the early 1990s amidst a global M&A boom. In the jointly authored third chapter, we relate this cash-use to firms' cash stockpiles, firm innovation, and growth by posing a general equilibrium theory of R&D-intensive firm cash stockpiling and use in M&As. Cash bids in M&As close faster than those financed externally, reducing the risk of competing offers and trade breakdowns. A higher common-value component in M&A arising from transferable productivity of firms’ intangible assets boosts M&A competition and serial acquirer cash-stockpiles. Calibrated to the U.S. economy, we find that increasing transferable productivity differences and M&A competition can account for the majority of aggregate firm cash stockpiles since 1990.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".